Blind source separation with adaptive learning rates for image encryption

In this study, we present a technique for image encryption problem based on the underdetermined blind source separation (BSS) principle with adaptive learning rates. The encryption process is transferred as the underdetermined BSS problem and is treated by means of the key images to achieve decryption. By properly generating the key images and constructing the underdetermined mixing matrix, the proposed BSS technique can achieve the security. The proposed BSS with adaptive learning rates approach is implemented by the interval type-2 fuzzy cerebellar model articulation controller (T2FCMAC) and particle swarm optimization. The T2FCMAC system is a more generalized system with better learning ability to provide the adaptive learning rate of the BSS. Besides, the particle swarm optimization is utilized to enhance the performance of convergence. Computer simulation results are shown to illustrate the effectiveness of the proposed approach.